Geneticization of deviant behavior and consequences for stigma: the case of mental illness

One likely consequence of the genetics revolution is an increased tendency to understand human behavior in genetic terms. How might this "geneticization" affect stigma? Attribution theory predicts a reduction in stigma via reduced blame, anger, and punishment and increased sympathy and help. According to "genetic essentialist" thinking, genes are the basis of human identity and strongly deterministic of behavior. If such ideas are commonly accepted, geneticization should exacerbate stigma by increasing perceptions of differentness, persistence, seriousness, and transmissibility, which in turn should increase social distance and reproductive restrictiveness. I test these predictions using the case of mental illness and a vignette experiment embedded in a nationally representative survey. There was little support for attribution theory predictions. Consistent with genetic essentialism, genetic attributions increased the perceived seriousness and persistence of the mental illness and the belief that siblings and children would develop the same problem. Genetic attribution did not affect reproductive restrictiveness or social distance from the ill person but did increase social distance from the person's sibling, particularly regarding intimate forms of contact involving dating, marriage, and having children.     

A multimethod assessment of personal space development in female and male,black and white children

In order to obtain evidence of the effects of demographic parameters and measurement techniques on personal space, several different assessments were used to determine the impact of subject age, race, and sex on interpersonal distancing. Thirteen different variables representing stimulation, paper-and-pencil, and behavioral techniques indicated that as age increased, personal space requirements decreased, particularly for whites. Blacks as compared to whites required less space at age 7, and mixed-sex dyads tended to require more space than same-sex dyads. The results thus indicate that while some previous findings appear to be measurement method specific, others show intermethod consistency.     

A Flexible Evaluative Framework for Order Picking Systems

This paper develops a novel framework to evaluate the integral performance of order picking systems with different combinations of storage and order picking policies. The warehousing literature on order picking mostly considers minimizing either elapsed time or distance as the sole objective, whereas warehouse managers in a supply chain have to look beyond single‐dimensional performance and consider trade‐offs among different criteria. Thus managers still need a unified and efficient framework to select a portfolio of appropriate order picking policies from a multi‐criteria and contextual perspective. Our framework—combining data envelopment analysis, ranking and selection, and multiple comparisons—provides an efficient methodology to simultaneously analyze several interrelated problems in order picking systems with multiple performance attributes, such as service levels and operational costs. We demonstrate our approach through comprehensive evaluations of order picking policies in three low‐level, picker‐to‐parts rectangular warehouses facing demand variations.     

Delta Method,Moment Convergence,and Inference

Statistics, as functions of the observations, are usually given by well-behaved functions. This fact is used to obtain limit distributions for statistics whose components are given by asymptotically linear functions. These results are then extended to the moments of distributions, covariance matrices and confidence regions for parameters of interest. These regions may be used to test, through duality, hypothesis on these parameters. A theoretical application is presented.     

Generalized Jackknife-Based Estimators for Univariate Extreme-Value Modeling

In this article, we revisit the importance of the generalized jackknife in the construction of reliable semi-parametric estimates of some parameters of extreme or even rare events. The generalized jackknife statistic is applied to a minimum-variance reduced-bias estimator of a positive extreme value index—a primary parameter in statistics of extremes. A couple of refinements are proposed and a simulation study shows that these are able to achieve a lower mean square error. A real data illustration is also provided.     

On sufficient conditions for the strong consistency of least-squares estimates

The strong consistency of the least-squares estimates in regression models is obtained when the errors are i.i.d. with absolute moment of order r, 0<r? 2. The assumptions presented for the random error sequence will permit us to obtain improvements of the conditions on the regressors in order to obtain the strong consistency of the least-squares estimates in linear and nonlinear regression models.     

Strong consistency of least squares estimates with i.i.d. errors with mean values not necessarily defined

We establish strong consistency of the least squares estimates in multiple regression models discarding the usual assumption of the errors having null mean value. Thus, we required them to be i.i.d. with absolute moment of order r, 0<r<2, and null mean value when r>1. Only moderately restrictive conditions are imposed on the model matrix. In our treatment, we use an extension of the Marcinkiewicz–Zygmund strong law to overcome the errors mean value not being defined. In this way, we get a unified treatment for the case of i.i.d. errors extending the results of some previous papers.     

Creating Organisational Strength from Operationalising Restrictions: Welfare Non-profit Organisations in the Russian Federation

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - The work of non-profit organisations (NPOs) in non-democratic country contexts tends to be judged on their contribution to...